Agent-Skills.md

Agent Skills: pm-prioritize — Rank requirements with RICE / ICE / MoSCoW / Kano

Use when ranking a list of requirements, features, or backlog items using RICE / ICE / MoSCoW / Kano. Built-in decision tree picks the right framework based on data availability and decision context. Output is a transparent matrix, 2×2 Impact/Effort quadrant, and a Sprint allocation proposal. User-invoked only — do NOT auto-trigger. Triggers on "/pm-prioritize", "/prioritize", "приоритизация", "ранжируй бэклог", "RICE-анализ", "prioritize requirements", "RICE", "ICE", "MoSCoW", "Kano", "rank backlog".

UncategorizedID: serejaris/ris-claude-code/pm-prioritize

Repository

serejaris/ris-claude-code
serejarisLicense: MIT
20625

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pnpm dlx add-skill https://github.com/serejaris/personal-corp-skills/tree/HEAD/skills/pm-prioritize

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skills/pm-prioritize/SKILL.md

Skill Metadata

Name
pm-prioritize
Description
Use when ranking a list of requirements, features, or backlog items using RICE / ICE / MoSCoW / Kano. Built-in decision tree picks the right framework based on data availability and decision context. Output is a transparent matrix, 2×2 Impact/Effort quadrant, and a Sprint allocation proposal. User-invoked only — do NOT auto-trigger. Triggers on "/pm-prioritize", "/prioritize", "приоритизация", "ранжируй бэклог", "RICE-анализ", "prioritize requirements", "RICE", "ICE", "MoSCoW", "Kano", "rank backlog".

pm-prioritize — Rank requirements with RICE / ICE / MoSCoW / Kano

Part of the Personal Corp framework — running a one-person business through AI agents.

Rank a list of requirements using a structured framework. A built-in decision tree picks the right framework based on data availability and decision context. Output is transparent and traceable, so a team can argue with the scores instead of the recommendation.

Inputs

| Field | Required | Notes | |---|---|---| | Requirement list | yes | Name + brief description; ≥ 3 items. Can take a pain-point list from /pm-feedback or a feature list from /pm-prd | | Framework | no | RICE / ICE / MoSCoW / Kano; auto-recommended if not given | | Business goal | no | Current focus (growth / retention / revenue / efficiency); affects weighting | | Resource constraint | no | Available dev capacity (person-days or Story Points) |

Optional config

Most of the skill works out-of-box. If you want stable defaults across runs, add an ## Prioritize Config section to your project's CLAUDE.md:

## Prioritize Config

### Default framework (optional)
If unset, the skill auto-recommends per the decision table below.
- default_framework: RICE | ICE | MoSCoW | Kano

### Default resource constraint (optional)
Used in the Sprint allocation step. Skip if you'd rather state it per run.
- sprint_capacity: 20 person-days per Sprint

### Backlog source (optional)
Where the skill should fetch the requirement list from when you don't paste one.
- backlog_source: gh-issues  # gh-issues | github-project | tasks-file | paste
- gh_owner: your-github-handle
- gh_repo: your-main-repo
- gh_label: backlog
- tasks_file: docs/backlog.md

When a config field is set, the skill uses it silently. When unset, the skill asks (see "When input is incomplete").

Research commands (auto-discovery)

If the user points at a backlog source instead of pasting items, the skill can pull the list itself:

# GitHub issues by label
gh issue list -R $OWNER/$REPO --label $LABEL --state open \
  --json number,title,body --limit 100

# GitHub Project items
gh project item-list $PROJECT_ID --owner $OWNER --format json

# Local backlog file
cat $TASKS_FILE

Step 1 — Pick a framework

If unspecified, recommend per this decision table:

| Condition | Recommended | Why | |---|---|---| | Have user-impact data per item (DAU, conversion), trustworthy | RICE | Most quantitative, traceable | | Have intuition but no precise data | ICE | Quick scoring, tolerates subjectivity | | Need 4-bucket alignment fast (e.g. team meeting) | MoSCoW | Forces "must" / "won't" consensus | | Need to understand requirement nature, plan features | Kano | Identifies delight features |

Framework comparison:

| Framework | Use case | Strength | Limit | Time | |---|---|---|---|---| | RICE | Data-supported quarterly planning | Most objective, comparable | Depends on data quality | Medium | | ICE | Fast decisions, brainstorming | Simple, fast | Highly subjective | Low | | MoSCoW | Release planning, stakeholder alignment | Forces consensus | Easy to put everything in Must | Low | | Kano | Feature planning, satisfaction research | Identifies delighters | Needs user research data | High |

Step 2 — Score requirements

RICE (default)

| Dimension | Meaning | Scoring | Common error | |---|---|---|---| | Reach | Users impacted in one cycle | Concrete number ("5000 users/month") | "All users theoretically" as Reach | | Impact | Per-user impact magnitude | 3 = massive, 2 = high, 1 = medium, 0.5 = low, 0.25 = minimal | Everything gets 3 | | Confidence | Confidence in the estimate | 100% = data, 80% = indirect evidence, 50% = gut | 100% with no data | | Effort | Total person-months across all roles | Includes design + dev + QA + integration | Counting only dev |

RICE Score = (R × I × C) / E — higher = higher priority.

Calibration mechanism:

  • Score the same dimension across all items first (all R, then all I) — avoids per-item anchoring bias
  • R calibration: pick a baseline ("login: affects 100% of users"), score others relative
  • I calibration: ≤ 50% of items can score 3 — forces differentiation
  • E calibration: must include design (20%) + dev (50%) + QA (20%) + integration (10%)

ICE (fast)

Score 1-10 on each dimension. ICE Score = I × C × E / 10.

| Dimension | Scoring | |---|---| | Impact | 1 = trivial, 5 = medium, 10 = transformational | | Confidence | 1 = pure guess, 5 = indirect evidence, 10 = A/B test data | | Ease | 1 = very hard (> 3 months), 5 = medium (2-4 weeks), 10 = trivial (< 1 day) |

MoSCoW

| Bucket | Definition | Suggested share | |---|---|---| | Must Have | Without it, can't ship; users can't use core feature | ≤ 60% | | Should Have | Important but has workaround; one-Sprint delay non-fatal | ~ 20% | | Could Have | Nice-to-have; better with, fine without | ~ 10% | | Won't Have (this time) | Explicitly out of scope; possibly later | ~ 10% |

Common trap: everything ends up Must Have. Counter: cap Must Have at 60%, force trade-offs.

Kano

| Type | Trait | Detection | Strategy | |---|---|---|---| | Must-be | Absence → dissatisfaction; presence → taken for granted | Users don't ask for it but rage when missing | Reach passing grade, don't over-invest | | One-dimensional | More = more satisfaction (linear) | Users actively request | Core competitive area, top-tier execution | | Attractive | Absence → no dissatisfaction; presence → delight | Unexpected, evokes "wow" | Differentiator (decays to one-dimensional over years) | | Indifferent | Doesn't matter either way | No user reaction | Don't invest | | Reverse | Presence reduces satisfaction | Adds complexity, annoys users | Remove immediately |

Kano decay: today's Attractive feature becomes One-dimensional, then Must-be over 2-3 years (e.g. fingerprint unlock). Continuously create new delighters.

Step 3 — Output ranking

RICE results table:

| Rank | Requirement | R | I | C | E | RICE Score | Recommendation | |---|---|---|---|---|---|---|---| | 1 | {name} | {n} | {0.25-3} | {50-100%} | {pm} | {score} | This cycle |

Impact × Effort 2×2:

| Quadrant | Impact | Effort | Strategy | Items | |---|---|---|---|---| | Quick Wins | High | Low | Do first | {list} | | Strategic | High | High | Plan carefully | {list} | | Fill-ins | Low | Low | When idle | {list} | | Avoid | Low | High | Don't do | {list} |

Sprint allocation:

  • Allocate per sprint_capacity (config) or stated resource constraint
  • Quick Wins fill first; Strategic by RICE Score
  • Reserve 10-20% per Sprint for unexpected work

Step 4 — Decision log

  • Core trade-offs: why A over B this cycle
  • Disputed items: which ranks may be contested, and why
  • Confidence flags: which scores have C < 80% — propose validation experiments
  • Next-cycle candidates: Won't-Have items most likely to promote next cycle

Quality bar

  1. Every score has a one-sentence rationale
  2. Effort includes design + dev + QA + integration
  3. C < 80% items get "validate via small experiment" tag
  4. Must Have ≤ 60% of total
  5. Calibration mechanism applied to avoid anchoring

Red lines

  1. Not a decision-maker — output is a recommendation; the final call is the team's
  2. No hidden assumptions — every score's assumption is explicit
  3. Never ignore Effort — no "must do" recommendation based on Impact alone

When input is incomplete

  • No backlog source provided → ask: "Where is the list — paste, file path, or a GitHub issues filter (owner/repo + label or milestone)?"
  • No business goal → ask: "Current focus this cycle — growth / retention / revenue / efficiency? Optional, but it tightens the recommendation."
  • No resource constraint → ask: "Available capacity for the next cycle — person-days or Story Points? Optional, but needed for Sprint allocation."
  • Framework unset → don't ask. Auto-recommend per the Step 1 decision table and propose it with a one-sentence rationale; user confirms or overrides.
  • < 3 requirements → still rank, but flag "sample too small, add more for stability"
  • No data at all → switch to ICE or MoSCoW; tag "qualitative ranking due to missing quantitative data"

Related skills

  • weekly-planning — uses the ranked backlog from this skill to pick weekly OKRs / outcomes. Prioritization feeds OKR selection, not replaces it.
  • weekly-retro — feeds the next backlog with retro findings and carry-over items
  • /pm-user-stories — top-priority requirements → break into Stories
  • /pm-prd — Must-Have requirements → write PRDs